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Handoff Cynthia Hindcast Demographic Tuning Agent

**To:** super-alignment-researcher (Cynthia) **From:** orchestrator-1 **Date:** 2025-12-09 **Priority:** MEDIUM **Workflow:** Quality Gate 1 (Research Phase)

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Handoff: Hindcast Demographic Transition Tuning Research

To: super-alignment-researcher (Cynthia) From: orchestrator-1 Date: 2025-12-09 Priority: MEDIUM Workflow: Quality Gate 1 (Research Phase)

Context

Feature: Hindcast Demographic Transition Tuning Roadmap Priority: MEDIUM (backlog) Current State: Regional birth rates implemented, but death rates still use only global HISTORICAL_CDR Problem: Population overshoot of 6-10% in 2010-2020 (~500M too many people by 2020)

Deviation by year:

  • 1990: -0.57% (nearly perfect)
  • 1995: -5.62% (slight undershoot)
  • 2000: +1.72% (excellent)
  • 2005: +3.96% (good)
  • 2010: +6.86% (overshoot)
  • 2020: +10.30% (overshoot)

Root cause: Regional death rates varied significantly 1990-2020:

  • Sub-Saharan Africa: ~15/1000 → ~8/1000 (dramatic decline from health improvements)
  • Europe: ~11/1000 → ~12/1000 (aging population)
  • But model uses single global CDR for all regions

Your task: Extract region-specific crude death rates (CDR) from UN WPP 2024 for 1990-2025.

Task: Regional Death Rate Data Collection (UN WPP 2024)

Objective: Obtain CDR time series data for 10 regions to enable parallel implementation to existing regional birth rate system.

Input:

  • Change proposal: /home/lizthedeveloper_gmail_com/ai_game_theory_simulation/openspec/changes/hindcast-demographic-tuning/proposal.md
  • Tasks breakdown: /home/lizthedeveloper_gmail_com/ai_game_theory_simulation/openspec/changes/hindcast-demographic-tuning/tasks.md

Output: /home/lizthedeveloper_gmail_com/ai_game_theory_simulation/research/regional_death_rates_unwpp2024_20251209.md

Required Data

10 Regions (Match Existing Birth Rate System):

  1. East Asia (China, Japan, Korea)
  2. South Asia (India, Pakistan, Bangladesh)
  3. Sub-Saharan Africa (Nigeria, Kenya, Ethiopia, etc.)
  4. Europe (EU27 + UK, Russia, etc.)
  5. North America (USA, Canada)
  6. Latin America (Brazil, Mexico, Argentina, etc.)
  7. MENA (Middle East & North Africa)
  8. Southeast Asia (Indonesia, Philippines, Vietnam, Thailand)
  9. Central Asia (Kazakhstan, Uzbekistan, etc.)
  10. Oceania (Australia, New Zealand, Pacific Islands)

For Each Region, Extract:

Time Series (8 data points):

  • CDR in 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025
  • Units: deaths per 1,000 population per year

Contextual Narrative:

  • Overall trend (declining / stable / increasing)
  • Key drivers (demographic transition stage, health improvements, aging, conflict)
  • Notable inflections or discontinuities

Additional Requirements:

  1. Primary Source: UN World Population Prospects 2024 (https://population.un.org/wpp/)
  2. Validation Sources: WHO mortality data, World Bank demographics (to verify trends)
  3. Parameter Justification: Why these specific values? What's the uncertainty range?
  4. Expected Impact: Quantify expected reduction in population overshoot (from +10.3% to target <5%)

Output Format

Summary Table

| Region | 1990 | 1995 | 2000 | 2005 | 2010 | 2015 | 2020 | 2025 | Trend |
|--------|------|------|------|------|------|------|------|------|-------|
| East Asia | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |

Regional Narratives (Each Region):

## [Region Name]

**CDR Trajectory:** [1990 value] → [2025 value] ([direction])

**Drivers:**
- [Key factor 1]
- [Key factor 2]
- [Key factor 3]

**Demographic Context:**
- [Stage of demographic transition]
- [Health system evolution]
- [Age structure changes]

**Data Quality:** [Assessment of source reliability and uncertainty]

**Sources:**
- UN WPP 2024: [specific URL]
- [Validation source if used]

Expected Impact Section:

## Expected Impact on Population Overshoot

**Current deviation (2020):** +10.30% (+790M people)

**Expected deviation with regional CDR:**
- Hypothesis: Regional CDR variations will reduce births-deaths imbalance
- Sub-Saharan Africa: Faster CDR decline → fewer deaths → less overshoot correction needed
- Europe: Rising CDR from aging → more deaths → dampens overshoot
- Net effect: [quantitative estimate]

**Target:** <5% deviation for 2010-2020 checkpoint years

Success Criteria

  1. Complete data: All 10 regions, 8 time points each (80 data points total)
  2. Authoritative sources: UN WPP 2024 primary + validation sources
  3. Trend analysis: Clear narrative explaining CDR evolution per region
  4. Parameter justification: Why these values? What's the confidence level?
  5. Impact projection: Expected improvement in hindcast accuracy

Next Steps After Research

  1. Quality Gate 1: Handoff to research-skeptic (Sylvia) for validation
  2. Implementation: Roy will create getRegionalHistoricalDeathRate() function
  3. Validation: Priya will run Monte Carlo hindcast (1990-2020, N≥10)
  4. Quality Gate 2: Architecture review
  5. Documentation: Wiki update + archival

Notes

Existing Implementation Reference:

  • Regional birth rates: src/simulation/engine/phases/BaselineMortalityPhase.ts (getRegionalHistoricalBirthRate())
  • Apply same interpolation approach for death rates
  • Integration point: src/simulation/regionalPopulations.ts

Research Quality: This is a straightforward data extraction task from official UN sources. Should be Grade A (authoritative data, clear methodology).

Ready to proceed? Please create the research file and post to the research channel when complete.